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  1. Muhammad A, Ali MAH, Turaev S, Abdulghafor R, Shanono IH, Alzaid Z, et al.
    Sensors (Basel), 2022 Oct 25;22(21).
    PMID: 36365875 DOI: 10.3390/s22218177
    This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment's borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm.
  2. Borikhonov B, Berdimurodov E, Kholikov T, Nik WBW, Katin KP, Demir M, et al.
    J Mol Model, 2024 Oct 02;30(11):359.
    PMID: 39356293 DOI: 10.1007/s00894-024-06157-y
    CONTEXT: This study addresses the development of sustainable pyridinium ionic liquids (ILs) because of their potential applications in agriculture and pharmaceuticals. Pyridinium-based ILs are known for their low melting points, high thermal stability, and moderate solvation properties. We synthesized three novel pyridinium-based ILs: 1-(2-(isopentyloxy)-2-oxoethyl)pyridin-1-ium chloride, 1-(2-(hexyloxy)-2-oxoethyl)pyridin-1-ium chloride, and 1-(2-(benzyloxy)-2-oxoethyl)pyridin-1-ium chloride. The biological activities of these compounds were evaluated through plant growth promotion, herbicidal, and insecticidal assays. Our results show that the benzyloxy derivative significantly enhances wheat and cucumber growth, whereas the isopentyloxy compound has potent herbicidal effects. Computational methods, including DFT calculations and molecular docking, were applied to understand the structure‒activity relationships (SARs) and mechanisms of action.

    METHODS: The computational techniques involved dispersion-corrected density functional theory (DFT) with the B3LYP functional and the 6-311G** basis set. Grimme's D3 corrections were included to account for dispersion interactions. The calculations were performed via GAMESS-US software. Quantum descriptors of reactivity, such as ionization potential, electron affinity, chemical potential, and electrophilicity index, were derived from the HOMO and LUMO energies. Molecular docking studies were conducted via the CB-Dock server via AutoDock Vina software to predict binding affinities to cancer-related proteins. Petra/Osiris/Molinspiration (POM) analysis was used to predict the drug likeness and other pharmaceutical properties of the synthesized ILs.

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